Two fundamental problems concern the handling of large n-gram language models:
indexing, that is, compressing the n-grams and associated satellite values without …

In recent years neural language models (LMs) have set state-of-the-art performance for
several benchmarking datasets. While the reasons for their success and their computational …

Many of the world's languages are falling out of use without a written record and minimal
linguistic documentation. Language documentation is a slow process and there are an …

In very recent years more attention has been placed on probing the role of pre-training data
in Large Language Models (LLMs) downstream behaviour. Despite the importance, there is …

Motivation Markov models with contexts of variable length are widely used in bioinformatics
for representing sets of sequences with similar biological properties. When models contain …

Abstract Hierarchical Pitman-Yor Process priors are compelling for learning language
models, outperforming point-estimate based methods. However, these models remain …

Succinct data structures involve the use of novel data structures, compression technologies,
and other mechanisms to allow data to be stored in extremely small memory or disk …

Abstract Space-efficient data structures are an active field of research that has found many
applications in combinatorial pattern matching and bioinformatics. The idea is to build data …

This thesis concerns the design of compressed data structures for the efficient storage of
massive datasets of integer sequences and short strings. The studied problems arise in …

A common task in bioinformatics algorithms is k-mer counting [MK11, KDD17]. Given a string
S, the problem is to count the frequency of each unique substring of length k in S. K-mer …